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MOTA: A Many-Objective Tuning Algorithm Specialized for Tuning under Multiple Objective Function Evaluation Budgets.

Antoine S Dymond1, Schalk Kok2, P Stephan Heyns3

  • 1Department of Mechanical and Aeronautical Engineering, University of Pretoria, South Africa antoine.dymond@gmail.com.

Evolutionary Computation
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Summary
This summary is machine-generated.

A new algorithm, Many-Objective Tuning Algorithm (MOTA), effectively tunes stochastic optimization algorithms. MOTA optimizes multiple performance measures across various evaluation budgets, improving search effectiveness for complex problems.

Keywords:
Tuningmany-objective optimizationmultiobjective optimizationobjective function evaluation budgets.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Algorithm Tuning

Background:

  • Selecting appropriate parameter values is crucial for effective optimization algorithm performance.
  • Tuning stochastic algorithms presents challenges due to noise and multiple performance objectives.
  • Existing methods may not adequately address the complexities of many-objective tuning.

Purpose of the Study:

  • To introduce a novel many-objective tuning algorithm (MOTA) for stochastic optimization algorithms.
  • To develop a specialized approach for optimizing multiple performance measures across diverse evaluation budgets.
  • To enhance the effectiveness and efficiency of parameter selection for optimization algorithms.

Main Methods:

  • Formulation of a tuning problem with speed objectives and decision variables.
  • A control parameter tuple assessment using single run history across multiple budgets.
  • Implementation of a preemptively terminating resampling strategy to handle noise.
  • Application of bi-objective decomposition for many-objective optimization.
  • Integration with differential evolution operators for parameter search.

Main Results:

  • The Many-Objective Tuning Algorithm (MOTA) was developed and tested.
  • MOTA demonstrated effectiveness in tuning algorithms like NSGA-II and MOEA/D.
  • The algorithm successfully optimized multiple performance measures over various evaluation budgets.
  • The specialized components of MOTA contributed to its successful application.

Conclusions:

  • MOTA provides an effective solution for the many-objective tuning of stochastic optimization algorithms.
  • The proposed methods address key challenges in tuning, including noise and multi-objective performance.
  • MOTA enhances the ability of practitioners to select suitable parameter values for their specific problems.